Thirion Bertrand, Duchesnay Edouard, Hubbard Edward, Dubois Jessica, Poline Jean-Baptiste, Lebihan Denis, Dehaene Stanislas
INRIA Futurs, Service Hospitalier Frédéric Joliot, 4, Place du Général Leclerc 91401 Orsay Cedex, France.
Neuroimage. 2006 Dec;33(4):1104-16. doi: 10.1016/j.neuroimage.2006.06.062. Epub 2006 Oct 9.
Traditional inference in neuroimaging consists in describing brain activations elicited and modulated by different kinds of stimuli. Recently, however, paradigms have been studied in which the converse operation is performed, thus inferring behavioral or mental states associated with activation images. Here, we use the well-known retinotopy of the visual cortex to infer the visual content of real or imaginary scenes from the brain activation patterns that they elicit. We present two decoding algorithms: an explicit technique, based on the current knowledge of the retinotopic structure of the visual areas, and an implicit technique, based on supervised classifiers. Both algorithms predicted the stimulus identity with significant accuracy. Furthermore, we extend this principle to mental imagery data: in five data sets, our algorithms could reconstruct and predict with significant accuracy a pattern imagined by the subjects.
传统的神经影像学推断在于描述由不同类型刺激引发和调节的大脑激活情况。然而，最近已经对一些范式进行了研究，在这些范式中进行的是相反的操作，即从激活图像推断与行为或心理状态相关的信息。在此，我们利用视觉皮层中广为人知的视网膜拓扑结构，从大脑激活模式中推断真实或想象场景的视觉内容。我们提出了两种解码算法：一种是显式技术，基于对视觉区域视网膜拓扑结构的现有知识；另一种是隐式技术，基于监督分类器。两种算法都能以显著的准确率预测刺激物的特征。此外，我们将这一原理扩展到心理意象数据：在五个数据集中，我们的算法能够以显著的准确率重建并预测受试者想象的模式。
